The Circle by Dave Eggers tells the story of a young woman working to navigate a fictional google-type corporation with its sights set on achieving universal surveillance. What the company hopes to achieve is a panopticon vision of society in which no one has any secrets from anyone else. Everything that everyone does is recorded, streamed, archived, and made available for anyone and everyone to see.
The ‘Three Wise Men’ who founded this fictional company — called ‘The Circle’ — represent three perspectives that we see guiding Big Data investments today:
(1) The gleeful possiblist – unconcerned with the consequences of technologies that are created, this kind of person is simply interested in exploring what is possible. They wash their hands of ethical or long-term implications, since those hinge a kind of widespread adoption that has nothing to do with innovation in itself.
(2) The business man – like the gleeful possiblist, the business man washes their hands of ethical consequences since, driven by a desire to grow the business, the success of the products and services created hinges on the desires of the masses.
(3) The utopian – this is the most thoughtful of the three perspectives, in that it is the only one to accept responsibility for the future. With respect to issues of of big data and surveillance, it sees privacy as a problem to be solved. With privacy comes secrets and the possibility of lies. With secrets and lies come conflict. Universal surveillance and absolutely transparency mean complete accountability, technology-mediated empathy, and freedom from fear.
The bulk of Egger’s work is spent describing a variety of social surveillance technologies and the burdens they place on users like the protagonist, Mae. In many ways, this world is described in compelling and favorable terms. The reader is not disturbed, but actually drawn into agreement with the dominant utopian ideology into which Mae is progressively indoctrinated. Of course, there are nay-sayers, dissenters, and outsiders but in this world they are the minority. In a world where everyone’s opinion matters, democracy is absolute. If democracy is a good thing, absolute democracy the the best thing.
As one would expect from a book like this, as the circle nears completion, Eggers uses the opportunity to explore several distopian themes, including the possibility of a future tyrannical leader making use of truth-telling technology for systematically manipulating public perception. But Eggers is really good at ambiguity. He does an excellent job of using his narrative to explore important themes and possibilities while at the same time withholding judgement. One does not get from Eggers the sense that the trajectory of our serveillance technologies and big data policies are good or bad. What is strongly affirmed, however, is the fact that we are responsible. The purpose of The Circle is to force its readers to reflect on the consequences of their behavior, to consider their part as complicit in shaping the future. The Circle is effective in underscoring the importance of making thoughtful decisions about how we use technology instead of being passive users in a world made of us rather than by us and for us.
Public concern about ‘big data’ frequently comes down to a vague and ill-defined sense of ‘ickiness.’ I’d like to briefly suggest a way to provides structure to this vague sentiment — let’s call it data dread. Provisionally, I would argue that public distrust of ‘big data’ comes down to major tension between two promises of the digital age. On the one hand, as Floridi notes, the advent of social media represents an “unprecedented opportunity to be more in charge of our social selves, to chose more flexible who the other people are whose thoughts and interactions create our social personality” (The Fourth Revolution, p. 64). In other words, the modern internet allows us not only to more carefully craft our identities, but also to more carefully curate our communities so that our self-representations are more likely to be recognized and accepted. We now have an unparalleled ability to ‘make ourselves’ in a way that resonates, not just with the existentialist philosophies of the 20th century, but also with Renaissance conceptions of man as infinitely fluid and self-determining. This is the first promise of the digital age.
On the other hand, however, the very technologies that allow us to make ourselves also — and necessarily — produce digital traces, on the basis of which it becomes possible for individuals to be tracked, and identities co-opted, The digital traces that people leave behind as a result of their efforts to forge their digital identities can also be used by algorithms to produce identities for which individuals are not themselves responsible, but that nevertheless have real effects. This is not necessarily a bad thing. In fact, this amounts to a second promise of the digital age: the promise of personalization. People want their experience of the world to be personalized. Who doesn’t want the world to revolve around them? But the problem with the algorithms that personalize experience is that they don’t actually care who you are or how you wish to be recognized. They don’t care about the identity you wish to construct. In personalizing our experiences of the world, algorithms also have a very real effect on our self-perceptions. In order to personalize our experiences, they must first personalize us.
The two promises of the digital age, then, are these: (1) a promise that individuals are free to construct themselves in whatever was they choose, and (2) a promise that digital traces will be used to personalize individual experiences, not just online, but in the ‘real world’ as well. Our experience of dread comes from the fact that we are simultaneously promised an ability to make ourselves at the same time as we are promised that our selves will be algorithmically made on our behalf. At the same time as we represent ourselves in such a way as to be recognized and acknowledged by others in the ‘real world,’ algorithms are representing us to ourselves and others, making judgements about who we are and what we want, and intervening in our lives through nudges, recommendations, and other automated events.
For Hegel, people come to recognize themselves as selves through two basic sources: labor and others. I know that I am a self because I can recognize myself in the work of my hands, and I know that I am a self because of the fact that others relate to me as such. In the former case, I am in complete control over the self I create. In the latter, the self that I create is a function of the a negotiation. Now, for the first time, it has become possible for people to be entirely — and voluntary — excluded from the processes by which their identities are constituted. In the social world, our identities and the consequences of our behaviors may not be our own, but they are nonetheless a function of a kind of ongoing negotiation with other people. When machines start to make judgements about us, any sense of negotiation is lost.
Perhaps our sense of ‘ickiness’ in the face of big data — our sense of data dread — is not a function of data itself. Perhaps it is a function of the fact that there is a contradiction at the heart of so-called ‘social media’ — what Floridi calls Information and Communication Technologies (ICT). It is not just that digital traces from ICTs make it possible for identities to be algorithmically co-opted. The algorithmic co-option of personal identity is a necessary feature of modern-day social media technologies in the absence of which those technologies would cease to function.
7 November 2014 Microsoft and Other Firms Pledge to Protect Student Data Fourteen companies, including Microsoft and Mifflin Harcourt, Amplify, and Edmodo, have pledged to adopt nationwide policies that will restrict and protect data collected from K-12 students. The group in pledging not to (1) sell student information, (2) target students with advertisements, or (3) compile personal student profiles unless authorized by parents or schools. The pledge, which is not legally binding, was developed by the Future of Privacy Forum.
6 November 2014 Lecturer Calls for Clarity in Use of Learning Analytics Sharon Slade (Open University) talks about her university’s effort to develop and ethical policy on the use of student data, that attempts to carefully address conflicting student concerns: (1) concerns about institutional ‘snooping’ on the one hand, and (2) an interest in personalized modes of communication. The Ethical Use of Student Data for Learning Analytics Policy produced at the Open University is the first of its kind, and the result of an exemplary effort that should be repeated widely.
6 November 2014 Echo360 Appoints Dr. Bradley S. Fordham as Global Chief Technology Officer Echo360, an active learning and lecture capture platform, has appointed Dr. Fordham as Global Technology Officer. With a wealth of industry and scholarly experience, Dr. Fordham will add significant expertise, legitimacy, and exposure to the platform. The this is the latest in a series of recent investments in developing the platform’s real-time analytics capabilities, which until recently, have been rather limited and unsophisticated.
6 November 2014 Harvard Researchers Used Secret Cameras to Study Attendance. Was That Unethical? In the spring of 2013, cameras in 10 Harvard classrooms recorded one image per minute, and the photographs were scanned to determine which seats were filled. The study rankled computer-science professor, Harry R. Lewis, who viewed the exercise as an obvious intrusion into student privacy. George Siemens notes that attendance data is the ‘lowest of the low,’ and notes that the level of surveillance taking place in online courses far exceeds what was collected as part of the attendance-tracking exercise. Since Lewis raised his concerns, Harvard has committed itself to reaching out to every faculty member and student whose image may have been captured to inform them of the research, a not-so-easy effort, as images were captured anonymously and have subsequently been destroyed as part of the research methodology.
5 November 2014 Disadvantages Students in Georgia District Get Home Internet Service Fayette County Schools in Georgia have partnered with Kajeet to give Title 1 students a Kajeet SmartSpot so that they can access online textbooks, apps, email, documents, sites, and their teachers while outside of school. The mobile hotspot works with the Kajeet cloud service and allows districts and schools to restrict access according to site- and time- base rules. The service also monitors student activity and provides teachers and administrators with learning analytics reports.
1 November 2014 Track Your Child’s Development Easily In May 2011, Jayashankar Balaraman — a serial entrepreneur with a background in advertising and marketing — moved into the education space with the launch of KNEWCLEUS, which in just three years has grown to become India’s largest parent-school engagement platform. The platform’s success is a result of the ease with which it makes parent-teacher communication, and the analytics engine that monitors student performance, identifies areas in need of remediation, and recommends relevant content.
Does Exercise (and Learning) Count If Not Counted? by Joshua Kim Kim asks the age-old question, “If I exercise and my fitness app does not record my steps, did my exercise ever happen?” He wonders about how the ability to track certain forms of activity, including learning activity, ends up altering behavior and shifting values on the basis of ‘trackability.’ The danger here, cautions Kim, is that we may come to conflate good teaching with digital practices that are more amenable to datafication.
10 Hottest Technologies in Higher Education by Vala Afshar Afshar summarizes the hottest technologies discussed by CIOs at the 2014 Annual EDUCUASE conference last month. Included in the list are wifi, social media, badges, analytics, wearables, drones, 3D printing, digital courseware, Small Private Online Courses (SPOCs), and virtual reality. Although analytics is included as one of many trends, it of course is also a major driver for each of these technologies as well.
Schools keep track of students’ online behavior, but do parents even know? by Taylor Armerding A truly exceptional review of literature and debates surrounding the collection and use of data from K-12 students. What kinds of data are a school’s ‘business’ to collect? How does an institution ensure informed consent, when privacy policies are often so complex as to be inaccessible by many parents? What is a school’s responsibility if it discovers something with implications for student success? Are schools ‘grooming kids for a lifetime of surveillance?’
Why I’m Voting ‘Yes’ on the Smart Schools Bond Act, Proposition 3 by Leonie Haimson New York Proposition 3 (also known as the Smart Schools Bond Act) would allow the sale of bonds top generate $2 billion statewide for capital funding. In spite of her resistance to using bond revenue to purchase electronic devices in schools (one of the key ways in which the bond revenues are meant to be spent), Haimson notes the urgent need that many schools have for an injection of funding, and notes that the finds may be spent in a wide variety of ways. She raises a concern about the proliferation of technolgies driven by companies interested in educational data mining, but notes that, thanks to the Children’s Online Privacy Protection Act, all parents have the right to opt out of any online data-mining instructional or testing program that collects personal data, whether their children participate in this program at school or home.
In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real–time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self–regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the culture, technological infrastructure, and teaching practices of higher education, from assessment–for–accountability to assessment–for–learning, cannot be achieved through piecemeal implementation of new tools. We propose here that the challenge of successful institutional change for learning analytics implementation is a wicked problem that calls for new adaptive forms of leadership, collaboration, policy development and strategic planning. Higher education institutions are best viewed as complex systems underpinned by policy, and we introduce two policy and planning frameworks developed for complex systems that may offer institutional teams practical guidance in their project of optimizing their educational systems with learning analytics.
In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.
Simon Fraser University (Victoria, BC, Canada) Tenure Track Position In Educational Technology And Learning Design – The Faculty of Education, Simon Fraser University (http://www.sfu.ca/education.html) seeks applications for a tenure-track position in Educational Technology and Learning Design at the Assistant Professor rank beginning September 1, 2015, or earlier. The successful candidate will join an existing complement of faculty engaged in Educational Technology and Learning Design, and will contribute to teaching and graduate student supervision in our vibrant Masters program at our Surrey campus and PhD program at our Burnaby campus. DEADLINE FOR APPLICATION: December 1, 2014
NEW!University at Buffalo (Buffalo, NY, USA) Associate for Institutional Research/Research Scientist: Online Learning Analytics – The University at Buffalo (UB), State University of New York seeks a scholar in online learning analytics to join its newly formed Center for Educational Innovation. Reporting to the Senior Vice-Provost for Academic Affairs, the Center for Educational Innovation has a mission to support and guide the campus on issues related to teaching, learning and assessment, and at the same time serves as a nexus for campus-wide efforts to further elevate the scholarship of and research support for pedagogical advancement and improved learning. The Research Scientist in online learning analytics will work in the area of Online Learning within the department and join a campus-wide network of faculty and researchers working on “big data”. DEADLINE FOR APPLICATION: December 6, 2014
NEW!University of Boulder Colorado (Boulder, Colorado, USA) Multiple Tenure Track Positions in Computer Science – The openings are targeted at the level of Assistant Professor, although exceptional candidates at higher ranks may be considered. Research areas of particular interest include secure and reliable software systems, numerical optimization and high-performance scientific computing, and network science and machine learning. DEADLINE FOR APPLICATION: Posted Until Filled
University of Technology, Sydney (Sydney, AUS) Postdoctoral Research Fellow: Academic Writing Analytics – Postdoctoral research position specialising in the use of language technologies to provide learning analytics on the quality of student writing, across diverse levels, genres and domains DEADLINE FOR APPLICATION: Posted Until Filled
University of Michigan (Ann Arbor, MI) Senior Digital Media Specialist – The University of Michigan is seeking a qualified Senior Digital Media Specialist to create digital content in support of online and residential educational experiences for the Office of Digital Education & Innovation (DEI). DEADLINE FOR APPLICATION: Posted Until Filled
NYU Steinhardt School of Culture, Education,and Human Developments Center for Research on Higher Education Outcomes (USA) 12-month postdoctoral position – available for a qualified and creative individual with interests in postsecondary assessment, learning analytics, data management, and institutional research.The Postdoctoral Fellow will be responsible for promoting the use of institutional data sources and data systems for the purpose of developing institutional assessment tools that can inform decision making and contribute to institutional improvement across New York University (NYU). DEADLINE FOR APPLICATION: Open Until Filled
A colleague recently sent me an email that included four questions that he suggested were the most concerning to both data management companies and customers: *
Big Data Tools – What’s working today? What’s next?
Big Data Storage – Do organizations have a manageable and scalable storage strategy?
Big Data Analytics – How are organizations using analytics to manage their large volume of data and put it to use?
Big Data Accessibility – How are organizations leveraging this data and making it more accessible?
These are bad questions.
I should be clear that the questions are not bad on account of the general concerns they are meant to address. Questions about tools, scalable storage, the ways in which data are analyzed (and visualized), and the availability of information are central to an organization’s long-term information strategy. Each of these four questions addresses a central concern that has very significant consequences for the extent to which available data can be leveraged to meet current informational requirements, but also future capacity. These concerns are good and important. The questions, however, are still bad.
The reason these questions are bad (okay, maybe they’re not bad…maybe I just don’t like them) is that they are unclear about their terms and definitions. In the first place, they imply that there is a separation between something called ‘Big Data’ and the tools, storage, analytics (here used very loosely), and accessibility necessary to manage it. In actual fact, however, there is no such ‘thing’ as Big Data in the absence of each of those four things. Transactional systems (in the most general sense, which also includes sensors) produce a wide variety of data, and it is an interest in identifying patterns in this data that has always motivated empirical scientific research. In other words, it is data, and not ‘Big Data’ that is our primary concern.
The problem with data as objects is that, until recently, we have been radically limited in our ability to capture and store them. A transactional system may produce data, but how much can we capture? How much can we store? For how long? Until recently, technological limitations have radically limited our ability to capture, store, and analyze the immense quantities of data that are generated, and have meant working with samples, and using inferential statistics to make probable judgements about a population. In the era of Big Data, these technological limitations are rapidly disappearing. As we increase our capacity to capture and store data, we increasingly have access to entire populations. A radical increase in available data, however, is not yet ‘Big Data.’ It doesn’t matter how much data you can store if you don’t also have the capacity to access it. Without massive processing power, sophisticated statistical techniques, and visualization aids, all of the data we collect is for naught, pure potentiality in need of actualization. It is only once we make population data meaningful in its entirety (not sampling from our population data) through the application of statistical techniques and sound judgement that we have something that can legitimately be called ‘Big Data.’ A datum is a thing given to experience. The collection and visualization of a population of data produces another thing given to experience, a meta-datum, perhaps.
In light of these brief reflections, I would like to propose the following (VERY) provisional definition of Big Data (which resonates strongly, I think, with much of the other literature I have read):
Big Data is the set of capabilities (capture, storage, analysis) necessary to make meaningful judgements about populations of data.
By way of closing, I think it is also important to distinguish between ‘Big Data’ on the one hand, and ‘Analytics’ on the other. Although the two are often used in conjunction with each other, it is important to note that using Big Data is not the same as doing analytics. Just as the defining characteristic of Big Data above in increased access (access to data populations instead of samples), so to does analytics. In the past, the ability to make data-driven judgements meant either having some level of sophisticated statistical knowledge oneself, or else (more commonly) relying upon a small number of ‘data gurus,’ hired expressly because of their statistical expertise. In contrast to more traditional approaches to institutional intelligence, which involve data collection, cleaning, analysis, and reporting (all of which took time), analytics toolkits quickly perform these operations in real-time, and make use of visual dashboards that allow stakeholders to make timely and informed decisions without also having the skills and expertise necessary to generate these insights ‘from scratch.’
Where Big Data gives individuals access to all the data, Analytics makes Big Data available to all
Big Data is REALLY REALLY exciting. Of course, there are some significant ethical issues that need to be addressed in this area, particularly as the data collected are coming from human actors, but from a methodological point of view, having direct access to populations of data is something akin to a holy grail. From a social scientific perspective, the ability to track and analyze actual behavior instead of relying on self-reporting about behavior on surveys can give us insight into human interactions that, until now, was completely impossible. Analytics, on the other hand, is something about which I am a little more ambivalent. There is definitely something to be said to encouraging data-driven decision-making, even by those with limited statistical expertise. Confronted by pretty dashboards that are primarily (if not exclusively) descriptive, without the statistical knowledge to ask even basic questions about significance (just because there appears to be a big difference between populations on a graph, it doesn’t necessarily mean that there is one), and with no knowledge about the ways in which data are being extracted, transformed, and loaded into proprietary data warehousing solutions, I wonder about the extent to which analytics do not, at least sometimes, just offer the possibility of a new kind of anecdotal evidence justified by appeal to the authority of data. Insights generated in this way are akin to undergraduate research papers that lean heavily upon Wikipedia because, if it’s on the internet, it’s got to be true.
In his Scientific American article, How Big Data is Taking Teachers Out of the Lecturing Business” Seth Fletcher describes the power of data-driven adaptive learning for increasing the efficacy of education while also cutting the costs associated with hiring teachers. Looking specifically at the case of Arizona State University, where computer-assisted learning has been adopted as an efficient way to facilitate the completion of general education requirements (math in particular), Fletcher describes a situation in which outcomes for students scores increase, teacher satisfaction improves (as teachers shift from lecturing to mediating), and profit is to be made by teams of data-scientists for hire.
There are, of course, concerns about computer-assisted adaptive learning, including those surrounding issues of privacy and the question of whether such a data-driven approach to education doesn’t tacitly favor STEM (training in which can be easily tested and performance quantified) over the humanities (which demands an artfulness not easily captured by even the most elaborate of algorithms). In spite of these concerns, however, Fletcher concludes with the claim that “sufficiently advanced testing is indistinguishable from instruction.” This may very well be the case, but his conception of ‘instruction’ needs to be clarified here. If by instruction Fletcher means to say teaching in general, then the implication of his statement is that teachers are becoming passé, and will at some point become entirely unnecessary. If, on the other hand, instruction refers only to a subset of activities that take place under the broader rubric of education, then there remains an unquantifiable space for teachers to practice pedagogy as an art, the space of criticism and imagination…the space of the humanities, perhaps?
As the title of Fletcher’s piece suggests, Big Data may very well be taking teachers out of the lecturing business, but it is not taking teachers out of the teaching business. In fact, one could argue that lecturing has NEVER been the business of teaching. In illustrating the aspects of traditional teaching that CAN be taken over by machines, big data initiatives are providing us with the impetus to return to questions about what teaching is, to clarify the space of teaching as distinct from instruction, and with respect to which instruction is of a lower-order even as it is necessary. Once a competence has been acquired and demonstrated, the next step is not only to put that competency to use in messy, real-world situations–situations in which it is WE who must swiftly adapt–but also to take a step back in order to criticize the assumptions of our training. Provisionally (ALWAYS provisionally), I would like to argue that it is here, where technê ends and phronesis begins, that the art of teaching begins as well.